Translation between cathodic and anodic neuromodulation parameter settings

ABSTRACT

A system for adjusting neuromodulation parameters used by a neuromodulator operably connected to a plurality of electrodes to modulate a neural target, may comprise a translation trigger detector configured to determine that a translation trigger has occurred, a first parameter setting storage configured to store first parameter settings for use by the neuromodulator to modulate the neural target, and a neuromodulation parameter translator. The neuromodulation parameter translator may be operably connected to the translation trigger detector to automatically translate the first parameter settings into a second parameter settings in response to determining the translation trigger has occurred, and replace the first parameter settings with the second parameter settings, or store the second parameter settings in a second parameter setting storage. Automatically translating may include either automatically translating from cathodic parameter settings to anodic parameter settings, or automatically translating from anodic parameter settings to cathodic parameter settings.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Patent Application Ser. No. 62/663,541, filed onApr. 27, 2018, which is herein incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates generally to medical devices, and moreparticularly, to neuromodulation systems, devices, and methods.

BACKGROUND

Neuromodulation, also referred to as neurostimulation, has been proposedas a therapy for a number of conditions. Examples of neuromodulationinclude Spinal Cord Stimulation (SCS), Deep Brain Stimulation (DBS),Peripheral Nerve Stimulation (PNS), and Functional ElectricalStimulation (FES). Implantable neuromodulation systems have been appliedto deliver such a therapy. An implantable neuromodulation system mayinclude an implantable neuromodulation, which may also be referred to asan implantable pulse generator (IPG), and one or more implantable leadseach including one or more electrodes. The implantable neuromodulationdelivers neuromodulation energy through one or more electrodes placed onor near a target site in the nervous system. An external programmingdevice is used to program the implantable neuromodulation withparameters controlling the delivery of the neuromodulation energy. Forexample, the neuromodulation energy may be delivered in the form ofelectrical pulses using parameters that specify spatial (where tostimulate), temporal (when to stimulate), and informational (patterns ofpulses directing the nervous system to respond as desired) aspects of apattern of pulses.

The human nervous systems use neural signals having sophisticatedpatterns. Also, as the condition of the patient may change whilereceiving a neuromodulation therapy, the neuromodulation applied to thepatient may need to be changed to maintain efficacy while minimizing theunintended and undesirable effects. Therefore, there is a need toprovide neuromodulation systems capable of such complex neuromodulationand a need to provide efficient and accurate programming of suchsystems.

SUMMARY

This Summary includes examples that provide an overview of some of theteachings of the present application and not intended to be an exclusiveor exhaustive treatment of the present subject matter. Further detailsabout the present subject matter are found in the detailed descriptionand appended claims. Other aspects of the disclosure will be apparent topersons skilled in the art upon reading and understanding the followingdetailed description and viewing the drawings that form a part thereof,each of which are not to be taken in a limiting sense. The scope of thepresent disclosure is defined by the appended claims and their legalequivalents.

An example (e.g. Example 1) of a system for adjusting neuromodulationparameters used by a neuromodulator operably connected to a plurality ofelectrodes to modulate a neural target, may comprise a translationtrigger detector configured to determine that a translation trigger hasoccurred, a first parameter setting storage configured to store firstparameter settings for use by the neuromodulator to modulate the neuraltarget, and a neuromodulation parameter translator. The neuromodulationparameter translator may be operably connected to the translationtrigger detector to automatically translate the first parameter settingsinto a second parameter settings in response to determining thetranslation trigger has occurred, and replace the first parametersettings with the second parameter settings, or store the secondparameter settings in a second parameter setting storage. Automaticallytranslating the first parameter settings into the second parametersettings may include either automatically translating from cathodicparameter settings to anodic parameter settings, or automaticallytranslating from anodic parameter settings to cathodic parametersettings. The neuromodulator may be configured to use cathodic parametersettings for the neuromodulation parameters to deliver cathodicmodulation to the neural target, and use anodic parameter settings forthe neuromodulation parameters to deliver anodic modulation to theneural target. For example, the anodic parameter settings may includeanodic monopolar, anodic pseudo-monopolar, and anodic major; and thecathodic parameter settings may include cathodic monopolar, cathodicpseudo-monopolar, and cathodic major.

In Example 2, the subject matter of Example 1 may optionally beconfigured such that the first parameter settings for theneuromodulation parameters include at least a polarity, an amplitude,and a fractionalization, and the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by changing the polarity for each activeelectrode in the plurality of electrodes.

In Example 3, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by multiplying the amplitude by a scalefactor. The scale factor may be determined based on whether the firstparameter settings are characterized as cathodic, characterized asanodic, or characterized as balanced or relatively balanced betweencathodic and anodic.

In Example 4, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by multiplying the amplitude by a scalefactor. An equation is used to determine the scale factor using one ormore of: the polarity within the first parameter settings; the amplitudewithin the first parameter settings; a pulse width within the firstparameter settings; a waveform within the first parameter settings; afrequency within the first parameter settings; at least oneburst-related parameter within the first parameter settings; or one ormore locations of modulation within the first parameter settings.

In Example 5, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by multiplying the amplitude by a scalefactor. The scale factor may be determined using a ratio between a firsttherapeutic range for the first parameter settings and a secondtherapeutic range for the second parameter settings, the firsttherapeutic range represents a range of amplitudes for the firstparameter settings that extends from a therapeutic threshold to a sideeffect threshold. The second therapeutic range may represent a range ofamplitudes for the second parameter settings that extends from atherapeutic threshold to a side effect threshold.

In Example 6, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by multiplying the amplitude by a scalefactor. A look-up table may be used to determine the scale factor, andat least one of the polarity, the pulse width, the amplitude, thewaveform, the frequency, the at least one burst-related parameter, orthe one or more locations of modulation may be used to index into thelook-up table. The scale factor may be a nearest value or aninterpolated value.

In Example 7, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to: construct a first table of I_(th) values for thefractionalization in the first parameter settings to characterizespatial points, wherein the first parameter settings provide astimulation field model; determine a maximum radius of the stimulationfield model at the amplitude for the first parameter settings; constructa second table of I_(th) values for the second parameter settings toprovide the stimulation field model with the maximum radius; determine,within the second table, a minimum value that provides the stimulationfield model that equals or approximately equals the maximum radius; anduse the determined minimum value, that provides the stimulation fieldmodel at the radius, as an amplitude for the second parameter settings.

In Example 8, the subject matter of Example 2 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to: construct a first table characterizing spatial points forthe fractionalization values in the first parameter settings, whereinthe first parameter settings provide a first stimulation field model;determine a volume of a stimulation field model at the amplitude in thefirst parameter settings; construct a second table characterizing thespatial points for the second parameter settings to provide a secondstimulation field model; determine an amplitude that provides the secondstimulation field model with a volume that equals or approximatelyequals the first stimulation field model; and use the determinedamplitude in the second parameter settings.

In Example 9, the subject matter of Example 1 may optionally beconfigured such that the system further comprises a user interfaceconfigured to receive at least one target region to be targeted using aneuromodulation field and zero or more avoidance regions to be avoidedusing the neuromodulation field. The controller may be configured todetermine fractionalization values for the second parameter settings tomodulate the at least one target region and avoid the zero or moreavoidance regions. The user interface may be further configured toreceive a polarity input indicating whether to provide anodicneuromodulation, cathodic neuromodulation or balanced or approximatelybalanced neuromodulation. The controller may be configured to control apolarity of neuromodulation provided by the neuromodulator according tothe received polarity input.

In Example 10, the subject matter of Example 9 may optionally beconfigured such that the neuromodulation parameter translator may beconfigured to automatically translate the first parameter settings intothe second parameter settings by changing the polarity of theneuromodulation, changing the fractionalization and changing theamplitude.

In Example 11, the subject matter of Example 10 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by optimizing the fractionalization andthe amplitude for the second parameter settings to modulate astimulation field model that corresponds to the first parametersettings.

In Example 12, the subject matter of Example 10 may optionally beconfigured such that the neuromodulation parameter translator isconfigured to automatically translate the first parameter settings intothe second parameter settings by: constructing a first I_(th) table forfirst fractionalization values in the first parameter settings tocharacterize spatial points, wherein the first parameter settings havean amplitude, a pulse width and a frequency; determining initial secondfractionalization values for the second parameter settings based on thefirst fractionalization values, wherein elements of the initial secondfractionalization values have an opposite polarity with respect toelements of the first fractionalization values, and constructing asecond table using the initial second fractionalization values;determining a scaling factor for the initial fractionalization values inthe second table to provide a scaled second table, wherein the scalingfactor produces a minimum sum of the squares difference between thefirst table and the scaled second table; and optimizing the initialsecond fractionalization values into a second fractionalization valuesin the second table that has a least sum of the squared differencebetween the first table and the scaled second table.

In Example 13, the subject matter of any one or any combination ofExamples 1-12 may optionally be configured such that the neuromodulationparameter translator is configured to use a fractionalization look-uptable and use: a virtual electrode to index into the fractionalizationlook-up table to produce multipolar fractionalizations; or a virtualelectrode to index into the fractionalization look-up table to producemonopolar fractionalizations.

In Example 14, the subject matter of any one or any combination ofExamples 9-13 may optionally be configured such that the system includesa one-polarity major multipolar fractionalization look-up table for usein producing anodic major multipolar fractionalization or cathodic majormultipolar fractionalization, a monopolar look-up table for use inproducing anodic monopolar or cathodic monopolar fractionalizations, anda polarity-balanced look-up table.

In Example 15, the subject matter of any one or any combination ofExamples 1-14 may optionally be configured such that the system furthercomprises a user interface configured to receive at least one of apolarity-specific region including a target region to be targeted usinga neuromodulation field of a polarity type and zero or more avoidanceregions to be avoided using the neuromodulation field of the polaritytype. The polarity-specific region may be specific to one or more ofcathodic major neuromodulation, anodic major neuromodulation, orbalanced or relatively balanced neuromodulation. The controller may beconfigured to automatically translate the first parameter settings intothe second parameter settings by determining fractionalization valuesfor the second parameter settings based on the polarity-specific region.

An example (e.g. “Example 16”) of a method for adjusting neuromodulationparameters used by a neuromodulator operably connected to a plurality ofelectrodes to modulate a neural target may include: determining that atranslation trigger has occurred; and in response to determining thatthe translation trigger has occurred, automatically translating firstparameter settings into second parameter settings. Automaticallytranslating the first parameter settings into the second parametersettings may include either automatically translating from cathodicparameter settings to anodic parameter settings, or automaticallytranslating from anodic parameter settings to cathodic parametersettings. The neuromodulator may be configured to use cathodic parametersettings for the neuromodulation parameters to deliver cathodicmodulation to the neural target, and may be configured to use anodicparameter settings for the neuromodulation parameters to deliver anodicmodulation to the neural target. For example, the anodic parametersettings may include anodic monopolar, anodic pseudo-monopolar, andanodic major; and the cathodic parameter settings may include cathodicmonopolar, cathodic pseudo-monopolar, and cathodic major.

In Example 17, the subject matter of Example 16 may optionally beconfigured such that the first parameter settings for theneuromodulation parameters may include at least a polarity, anamplitude, and a fractionalization. Automatically translating the firstparameter settings into the second parameter settings may includechanging the polarity for each active electrode.

In Example 18, the subject matter of Example 17 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include multiplying theamplitude by a scale factor. The scale factor may be determined based onwhether the first parameter settings are characterized as cathodic,characterized as anodic, or characterized as balanced or relativelybalanced between cathodic and anodic.

In Example 19, the subject matter of Example 17 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include multiplying theamplitude by a scale factor. An equation may be used to determine thescale factor using one or more of: the polarity within the firstparameter settings; the amplitude within the first parameter settings; apulse width within the first parameter settings; a waveform within thefirst parameter settings; a frequency within the first parametersettings; at least one burst-related parameter within the firstparameter settings; or one or more locations of modulation within thefirst parameter settings.

In Example 20, the subject matter of Example 17 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include multiplying theamplitude by a scale factor. The scale factor may be determined using aratio between a first therapeutic range for the first parameter settingsand a second therapeutic range for the second parameter settings. Thefirst therapeutic range may represent a range of amplitudes for thefirst parameter settings that extends from a therapeutic threshold to aside effect threshold, and the second therapeutic range may represent arange of amplitudes for the second parameter settings that extends froma therapeutic threshold to a side effect threshold.

In Example 21, the subject matter of Example 17 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include multiplying theamplitude by a scale factor. A look-up table may be used to determinethe scale factor, and at least one of the polarity, the pulse width, theamplitude, the waveform, the frequency, the at least one burst-relatedparameter, or the one or more locations of modulation may be used toindex into the look-up table. The scale factor may be a nearest value oran interpolated value.

In Example 22, the subject matter of Example 17 may optionally beconfigured such that the method further comprises: constructing a firsttable of I_(th) values for the fractionalization in the first parametersettings to characterize spatial points, wherein the first parametersettings provide a stimulation field model; determining a maximum radiusof the stimulation field model at the amplitude for the first parametersettings; constructing a second table of I_(th) values to characterizethe spatial points for the second parameter settings to provide thestimulation field model with the maximum radius; determining, within thesecond table, a minimum value that provides the stimulation field modelthat equals or approximately equals the maximum radius, and using thedetermined minimum value, that provides the stimulation field model atthe radius, as an amplitude for the second parameter settings.

In Example 23, the subject matter of Example 17 may optionally beconfigured such that the method further comprises: constructing a firsttable characterizing spatial points for the fractionalization values inthe first parameter settings, wherein the first parameter settingsprovide a first stimulation field model; determining a volume of astimulation field model at the amplitude in the first parametersettings; constructing a second table characterizing the spatial pointsfor the second parameter settings to provide a second stimulation fieldmodel; determining an amplitude that provides the second stimulationfield model with a volume that equals or approximately equals the firststimulation field model; and using the determined amplitude in thesecond parameter settings.

In Example 24, the subject matter of Example 16 may optionally beconfigured such that the method further comprises: receiving at leastone target region to be targeted using a neuromodulation field and zeroor more avoidance regions to be avoided using the neuromodulation field,and determining fractionalization values for the second parametersettings to modulate the at least one target region and avoid the zeroor more avoidance regions; and receiving a polarity input indicatingwhether to provide anodic neuromodulation, cathodic neuromodulation orbalanced or approximately balanced neuromodulation, and controlling apolarity of neuromodulation provided by the neuromodulator according tothe received polarity input.

In Example 25, the subject matter of Example 24 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include changing thepolarity of the neuromodulation, changing the fractionalization andchanging the amplitude.

In Example 26, the subject matter of Example 25 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include optimizing thefractionalization and the amplitude for the second parameter settings tomodulate a stimulation field model that corresponds to the firstparameter settings.

In Example 27, the subject matter of Example 25 may optionally beconfigured such that automatically translating the first parametersettings into the second parameter settings may include: constructing afirst I_(th) table for first fractionalization values in the firstparameter settings to characterize spatial points, wherein the firstparameter settings have an amplitude, a pulse width and a frequency;determining initial second fractionalization values for the secondparameter settings based on the first fractionalization values, whereinelements of the initial second fractionalization values have an oppositepolarity with respect to elements of the first fractionalization values,and constructing a second table using the initial secondfractionalization values; determining a scaling factor for the initialfractionalization values in the second table to provide a scaled secondtable, wherein the scaling factor produces a minimum sum of the squaresdifference between the first table and the scaled second table; andoptimizing the initial second fractionalization values into a secondfractionalization values in the second table that has a least sum of thesquared difference between the first table and the scaled second table.

In Example 28, the subject matter of Example 16 may optionally beconfigured such that the method further comprises using a virtualelectrode to index into the fractionalization look-up table to producemultipolar fractionalizations.

In Example 29, the subject matter of Example 16 may optionally beconfigured such that the method further comprises using steeringcoordinates, including z axis, rotation, and spread coordinates, toindex into the fractionalization look-up table to produce monopolarfractionalizations.

In Example 30, the subject matter of Example 16 may optionally beconfigured such that the method further comprises using a one-polaritymajor multipolar fractionalization look-up table to produce anodic majormultipolar fractionalization or cathodic major multipolarfractionalization.

In Example 31, the subject matter of Example 16 may optionally beconfigured such that the method further comprises using a monopolarlook-up table to produce anodic monopolar or cathodic monopolarfractionalizations.

In Example 32, the subject matter of Example 16 may optionally beconfigured such that the method further comprises using a monopolarlook-up table to produce polarity-balanced fractionalizations.

In Example 33, the subject matter of Example 16 may optionally beconfigured such that the method further comprises receiving at least oneof a polarity-specific region including a target region to be targetedusing a neuromodulation field of a polarity type and zero or moreavoidance regions to be avoided using the neuromodulation field of thepolarity type. The polarity-specific region may be specific to one ormore of cathodic major neuromodulation, anodic major neuromodulation, orbalanced or relatively balanced neuromodulation. Automaticallytranslating the first parameter settings into the second parametersettings may include determining fractionalization values for the secondparameter settings based on the polarity-specific region.

An example (e.g. “Example 34”) includes a non-transitorycomputer-readable medium having computer executable instructions storedthereon that, when executed by at least one processor, cause the atleast one processor to perform the instructions to adjustneuromodulation parameters for use by a neuromodulator operablyconnected to a plurality of electrodes to modulate a neural target, Theinstructions may be capable of performing the methods recited above. Forexample, the instructions may comprise cause the machine to: determinethat a translation trigger has occurred, and in response to determiningthat the translation trigger has occurred, automatically translate firstparameter settings into second parameter settings. Automaticallytranslating the first parameter settings into the second parametersettings may include either automatically translating from cathodicparameter settings to anodic parameter settings, or automaticallytranslating from anodic parameter settings to cathodic parametersettings. The neuromodulator may be configured to use cathodic parametersettings for the neuromodulation parameters to deliver cathodicmodulation to the neural target, and may be configured to use anodicparameter settings for the neuromodulation parameters to deliver anodicmodulation to the neural target.

In Example 35, the subject matter of Example 14 may optionally beconfigured such that the first parameter settings for theneuromodulation parameters include at least a polarity, an amplitude,and a fractionalization. Automatically translating the first parametersettings into the second parameter settings may include changing thepolarity for each active electrode.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates, by way of example and not limitation, an embodimentof a neuromodulation system.

FIG. 2 illustrates, by way of example and not limitation, an embodimentof a stimulation device and a lead system, such as may be implemented inthe neuromodulation system.

FIG. 3 illustrates, by way of example and not limitation, a programmingdevice, which may be an embodiment of the programming device andimplemented in neuromodulation system.

FIG. 4 illustrates, by way of example and not limitation, an embodimentof an implantable pulse generator (IPG) and an implantable lead system.

FIG. 5 illustrates, by way of example and not limitation, an embodimentof an IPG and an implantable lead system arranged to provide brainneuromodulation to a patient.

FIG. 6 illustrates, by way of example and not limitation, an embodimentof portions of a neuromodulation system.

FIG. 7 illustrates, by way of example and not limitation, an embodimentof implantable neuromodulation device and one or more leads of animplantable neuromodulation system, such as the implantable system.

FIG. 8 is a schematic diagram illustrating radial current steering alongvarious electrode levels along the length of the lead.

FIGS. 9A-9E illustrate, by way of example, and not limitation, variousexamples of leads with which radial current steering may be used.

FIG. 10 illustrates examples of fractionalizations to provide differentmodulation types.

FIG. 11 illustrates an example of a presentation of clinical effects andstimulation configuration on a user interface.

FIG. 12 illustrates a series of planes distributed around a lead havingelectrodes and threshold tables (e.g. I_(th) tables) for those planes.

FIG. 13 illustrates an embodiment of a system for adjustingneuromodulation parameters used by a neuromodulator operably connectedto a plurality of electrodes to modulate a neural target.

FIG. 14 illustrates functions of a translator such as may be implementedin the system of FIG. 13.

FIG. 15 is a block diagram illustrating a machine in the example form ofa computer system, within which a set or sequence of instructions may beexecuted to cause the machine to perform any one of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

The following detailed description of the present subject matter refersto the accompanying drawings which show, by way of illustration,specific aspects and embodiments in which the present subject matter maybe practiced. These embodiments are described in sufficient detail toenable those skilled in the art to practice the present subject matter.Other embodiments may be utilized and structural, logical, andelectrical changes may be made without departing from the scope of thepresent subject matter. References to “an”, “one”, or “various”embodiments in this disclosure are not necessarily to the sameembodiment, and such references contemplate more than one embodiment.The following detailed description is, therefore, not to be taken in alimiting sense, and the scope is defined only by the appended claims,along with the full scope of legal equivalents to which such claims areentitled.

Different types of neural structures have different reactions todifferent neuromodulation polarities. For example, cathodicneuromodulation may have a tendency to preferentially modulate neuronfibers, and anodic neuromodulation may have a tendency to preferentiallymodulate tissue inclusive of cell bodies. Although conventional DBS hasused cathodic neuromodulation, it is believed that anodicneuromodulation for DBS may benefit some patients more than cathodicstimulation. Thus, it is believed that a particular disease may responsebetter to anodic stimulation, and another disease may respond better tocathodic stimulation, and yet another disease may response better to amixed or balanced or nearly balanced neuromodulation (at least somepercentage of anodic neuromodulation and at least some percentage ofcathodic neuromodulation). Further, cathodic neuromodulation of oneregion may improve one or more symptoms of a disease and anodicneuromodulation of the same or different region may improve one or moreother symptoms of the disease. Additional properties of theneuromodulation therapy may also contribute to the preferentialmodulation of some tissue over other tissue. Examples of such propertiesmay include proximity of the tissue to an electrode, the size of theneural element, the trajectory/geometry of the neural element, theproximity of the cell body (or dendrites, or axon), the biophysicalproperties such as ion channels and distribution in the neural element,the synaptic machinery of the neural element, and the like.

Since different neural tissue have different responses to differentneuromodulation polarities, the process for programming modulationparameters to target some region(s) while avoiding other region(s) canbe complex. However, clinicians currently do not have any direction onhow to convert from cathodic settings to anodic settings.

Various embodiments provided herein automatically translate betweencathodic and anodic neuromodulation parameter settings. For example,various embodiments automatically translate cathodic settings, which mayhave been previously programmed into the neuromodulation system, intoanodic settings. Similarly, various embodiments may automaticallyconvert anodic settings into cathodic settings. For example, variousembodiments may be used to invert the polarity of the original settings,with or without changing the fractionalization of the current among theelectrodes. Various embodiments may automatically convert the cathodicsettings to anodic settings using known target and avoidance regions.Each region may have an associated weight, which may be shared betweenregions, a background weight, possibly voxelization settings, andpossibly a stimulation type. As will be discussed further below,stimulation type examples may include anodic monopolar, cathodicmonopolar, anodic major, cathodic major, and balanced. Some systemembodiments allow the user to set the neuromodulation polarity to any ofthese types.

In various examples, the neuromodulation system may include animplantable device configured to deliver neuromodulation therapies, suchas DBS, SCS and PNS including vagus nerve stimulation (VNS), and one ormore external devices configured to program the implantable device forits operations and monitor the performance of the implantable device.While DBS is discussed as a specific example, the present subject mattermay also be applied to program stimulation devices for deliveringvarious types of neuromodulation therapies.

The neuromodulation system may determine one or more stimulationparameters to modulate a target, such as a stimulation current and anelectrical current fractionalization across a plurality of electrodes.The current fractionalization refers to current distribution amongelectrodes, and may be represented by percentage cathodic current,percentage anodic current, or off (no current allocation). Althoughcurrent fractionalization is discussed in this document, it is to beunderstood that voltage or electrical energy may similarly befractionalized among the electrodes, which may result in a particularspatial distribution of the stimulation field.

FIG. 1 illustrates, by way of example and not limitation, an embodimentof a neuromodulation system 100. The system 100 may, for example, beconfigured for DBS applications. Such DBS configuration includes variousfeatures that may simplify the task of the user in programming thestimulation device 104 for delivering DBS to the patient, such as thefeatures discussed in this document. The illustrated system 100 includesa programming device 102, a neuromodulation device 104, and electrodes106. The electrodes 106 may be configured for placement on or near oneor more neural targets in a patient. The stimulation device 104 may beconfigured to be electrically connected to the electrodes 106 anddeliver neuromodulation energy, such as in the form of electricalpulses, to the one or more neural targets though the electrodes 106. Inan example, the neuromodulation device 104 controls the delivery ofneuromodulation energy according to a plurality of neuromodulationparameters, such as a selection of active electrodes for passingneuromodulation energy to the tissue, or stimulation pattern of theelectrical pulses, among others. In various examples, at least some ofthe neuromodulation parameters are programmable by a user, such as aclinician.

The programming device 102 may be configured to be in communication withthe neuromodulation device 104 via a wired or wireless link. Theprogramming device 102 may provide the user with accessibility touser-programmable parameters. In the illustrated example, theprogramming device 102 may include a user interface 108 that allows auser to control the operation of the system 100 and monitor theperformance of the system 100 as well as conditions of the patientincluding responses to the delivery of the neuromodulation. The user maycontrol the operation of the system 100 by setting and/or adjustingvalues of the user-programmable parameters. In various examples, theuser interface 108 may include a graphical user interface (GUI) thatallows the user to create and/or edit graphical representations ofvarious neuromodulation waveforms. The GUI may also allow the user toset and/or adjust neuromodulation fields each defined by a set ofelectrodes through which one or more electrical pulses represented by awaveform are delivered to the patient, The neuromodulation fieldsmay^(,) each be further defined by the current fractionalization acrossthe set of electrodes. In various examples, electrical pulses for astimulation period (such as the duration of a therapy session) may bedelivered to multiple neuromodulation fields.

In this document, a “user” includes a physician or other clinician orcaregiver who treats the patient using the system 100; a “patient”includes a person who receives, or is intended to receive,neurostimulation via the system 100. In various examples, the patientmay be allowed to adjust his or her treatment using system 100 tocertain extent, such as by adjusting certain therapy parameters andentering feedback and clinical effect information.

FIG. 2 illustrates, by way of example and not limitation, an embodimentof a stimulation device 204 and a lead system 208, such as may beimplemented in the neuromodulation system 100. The neuromodulationdevice 204 represents an embodiment of neuromodulation device 104, andincludes a neuromodulation output circuit 210 and a neuromodulationcontrol circuit 212. The neuromodulation output circuit 210 may produceand deliver electrical pulses. The neuromodulation control circuit 212may control the delivery of the electrical pulses from theneuromodulation output circuit 210 according to a plurality ofparameters. The lead system 214 includes one or more leads eachconfigured to be electrically connected to neuromodulation device 204and a plurality of electrodes (including electrode 206-1, 206-2, . . . ,206-N) distributed in the one or more leads. Each of the electrodes hasan electrically conductive contact providing for an electrical interfacebetween the neuromodulation output circuit 210 and patient tissue. Thenumber of leads within the lead system, the number of electrodes on theleads, the leady types, and the type of electrodes (e.g. ring,segmented) may vary among the various embodiments.

The electrical pulses may be delivered from the neuromodulation outputcircuit 212 through a set of electrodes selected from the electrodes206. In various examples, the electrical pulses may include one or moreindividually defined pulses, and the set of electrodes may beindividually definable by the user for each of the individually definedpulses or each of collections of pulse intended to be delivered usingthe same combination of electrodes. In various examples, one or moreadditional electrodes 214 (referred to as reference electrodes) may beelectrically connected to the neuromodulation device 204, such as one ormore electrodes each being a portion of or otherwise incorporated onto ahousing of the neuromodulation device 204. Electrodes on the housing maybe referred to as “can electrodes”. The neuromodulation may be deliveredas a unipolar, bipolar, or multipolar stimulation. Monopolar stimulationuses a monopolar electrode configuration with one or more electrodesselected from the electrodes within the lead system 208 and at least oneelectrode from electrode(s) 214. Bipolar stimulation uses a bipolarelectrode configuration with two electrodes selected from the electrodeswithin the lead system 208 and none of the electrode(s) 214. The bipolarstimulation may include balanced or unbalanced bipolar mode using a pairof electrodes on a lead, with the balancing current being applied to areference electrode. Some bipolar stimulation may approximate amonopolar field, and thus may be considered to be a substantiallymonopolar field or a pseudo-monopolar field. By way of example and notlimitation, a first electrode E1 may contribute 100% of the positivecurrent, a second electrode E2 may contribute a small percentage of thenegative current (e.g. <5%), and the can may contribute a largepercentage of the negative current (e.g. >95%). A substantiallymonopolar field may be characterized as a field having a cancontributing a threshold indicating a relatively high percentage of thecurrent for a given polarity. For example, the threshold may be 75% ormay be a percentage between 75% and 100%. Multipolar stimulation uses amultipolar electrode configuration with multiple (two or more)electrodes selected from electrodes within the lead system 208 and noneof electrode(s) 214.

FIG. 3 illustrates, by way of example and not limitation, a programmingdevice 302, which may be an embodiment of the programming device 102 andimplemented in neuromodulation system 100. The programming device 302may include a storage device 316, a programmer circuit 318, and a userinterface 308. The programmer circuit 318 may be a part of controlcircuitry of the programming device 302, and is configured to supportone or more functions allowing for programming of neuromodulationdevices, such as neuromodulation device 104 including its variousembodiments as discussed in this document. In various examples, theprogrammer circuit 318 may generate a plurality of neuromodulationparameters, collectively referred to as a neuromodulation configurationor neuromodulator settings, that control the delivery of the electricalpulses. In various examples, the neuromodulation configuration mayspecify a stimulation current (e.g., amplitude or energy of thestimulation) and an electrical current fractionalization across theplurality of electrodes. In some examples, the neuromodulationconfiguration may include a stimulation location and a stimulationcurrent that corresponds to a metric value. In various examples, theneuromodulation configuration may include a virtual electrode state thatspecifies a virtual electrode type, location of the virtual electrode ina coordinate space, and stimulation current associated with virtualelectrode voltage field and virtual electrode location. Electricalcurrent fractionalization across a plurality of electrodes may bedetermined based on the voltage field of the virtual electrode.

The storage device 316 may store information used by the programmercircuit 318, including the neuromodulation configuration. The userinterface 308 represents an embodiment of user interface 108, and may becoupled to the programmer circuit 318. In various examples, the userinterface 308 may allow for definition of a pattern of electrical pulsesfor delivery during a neuromodulation therapy session by creating and/oradjusting one or more waveforms using a graphical method. The definitionmay also include definition of one or more neuromodulation fields eachassociated with one or more pulses in the pattern of electrical pulses.In various examples, the user interface 308 may include a GUI thatallows the user to define the pattern pulses and perform other functionsusing graphical methods.

The circuits or subcircuits included in the neuromodulation system ordevices, and their variations discussed in this document, may beimplemented using a combination of hardware and software. For example,the circuits may be implemented using an application-specific circuitconstructed to perform one or more particular functions or ageneral-purpose circuit programmed to perform such function(s). Such ageneral-purpose circuit includes, but is not limited to, amicroprocessor or a portion thereof, a microcontroller or portionsthereof, and a programmable logic circuit or a portion thereof.

FIG. 4 illustrates, by way of example and not limitation, an embodimentof an implantable pulse generator (IPG) 404 and an implantable leadsystem, illustrated as two leads 408A and 408B. The IPG 404 representsan example implementation of neuromodulation device 204, and may includea hermetically-sealed IPG case 420 to house the electronic circuitry ofIPG 404. The IPG 404 may include an electrode 414A and may includeelectrode 414B formed on the IPG case 420. The IPG 404 may include anIPG header 422 for coupling the proximal ends of leads 408A and 408B.Electrodes 426 and/or 428 may each be referred to as a referenceelectrode or can electrode. The IPG 404 may be communicatively coupledto a programming device, such as the programmer device 102 or theprogramming device 302, and configured to generate and deliverneuromodulation energy according to the neuromodulator configurationgenerated by the programming device 102 or 302.

The illustrated lead system includes, by way of example and notlimitation, two implantable leads 408A and 408B. As illustrated in FIG.4A, the IPG 404 may be coupled to the implantable leads 408A-B at aproximal end of each lead. The distal end of each lead includeselectrical contacts or electrodes 406 for contacting a tissue sitetargeted for electrical neuromodulation. In various examples, one ormore of the electrodes 406 may be column electrodes (also known as ringelectrodes), or segmented electrodes circumferentially disposed on adirectional lead such as 408A or 408B.

The implantable leads and electrodes may be shaped and sized to provideelectrical neuromodulation energy to a neural target, such as a brain, anerve target of a spinal cord, or a peripheral nerve target.Neuromodulation energy may be delivered in a unipolar mode between anelectrode selected from electrodes 406 and another electrode selectedfrom electrodes 414A and 414B, or in a balanced or unbalanced bipolarmode using a pair, or more, of electrodes on the same lead (e.g., lead408A or lead 408B), with the balancing current being applied toreference electrodes 414A or 414B. Neuromodulation energy may bedelivered in an extended bipolar mode using one or more electrodes of alead (e.g., one or more electrodes of lead 408A) and one or moreelectrodes of a different lead (e.g., one or more electrodes of lead408B).

The electronic circuitry of IPG 404 may include a control circuit thatcontrols delivery of the neuromodulator energy. The control circuit mayinclude a microprocessor, a digital signal processor, applicationspecific integrated circuit (ASIC), or other type of processor,interpreting or executing instructions included in software or firmware.The neuromodulation energy may be delivered according to specified(e.g., programmed) modulation parameters. Examples of setting modulationparameters may include, among other things, selecting the electrodes orelectrode combinations used in the neuromodulation, configuring anelectrode or electrodes as the anode or the cathode for theneuromodulation, and specifying pulse parameters. Examples of pulseparameters include, among other things, the amplitude of a pulse(specified in current or voltage), pulse duration (e.g., inmicroseconds), pulse rate (e.g., in pulses per second), and parametersassociated with a pulse train or pattern such as burst rate (e.g., an“on” modulation time followed by an “off” modulation time), amplitudesof pulses in the pulse train, polarity of the pulses, etc.

The modulation parameters may additionally include fractionalizationacross electrodes. The fractionalization specifies distribution (e.g.,the percentage) of the neuromodulation current, voltage, or electricalenergy provided by an electrode or electrode combination, which affectthe spatial distribution of the resultant neuromodulation field. In anexample, current fractionalization specifies percentage cathodiccurrent, percentage anodic current, or off (no current allocation).Current may be fractionalized across the active electrodes, such thatactive electrodes may receive a respective current percentage.Non-active electrodes are “off” or contribute no current to theneuromodulation. In the monopolar case, the fractionalized currentsacross the active electrodes add up to 100%. In the bipolar ormultipolar cases, the fractionalized currents for at least one polarityadd up to 100%, with any remaining percentage being allocated to thereference electrodes. Control of the current in terms of percentageallows precise and consistent distribution of the current among theelectrodes even as the overall current amplitude for the parameter setis adjusted. In sonic examples, the current fractionalization may bedefined by assigning an absolute current value (e.g., in milliampere, ormA) rather than a percentage to each electrode. Control of the currentin terms of absolute values allows precise dosing of current througheach specific electrode. It is suited for changing the current onecontact at a time (and allows the user to do so) to shape theneuromodulation like a piece of clay (pushing/pulling one spot at atime).

The current fractionalization takes into account electrode/tissuecoupling differences, which are the differences in how the tissueunderlying each electrode reacts to electrical neuromodulation. Inaddition, electrodes on the distal portion of the lead may have lowergradient in the longitudinal direction, as electrical field strength maytaper down at the ends of the lead. Current fractionalization mayaccommodate variation in the tissue underlying those electrodes. Variousembodiments described herein implement a programmed algorithm todetermine the appropriate fractionalization to achieve a desiredneuromodulation field property.

FIG. 5 illustrates, by way of example and not limitation, an embodimentof an IPG 504 and an implantable lead system 508 arranged to providebrain neuromodulation to a patient. An example of IPG 504 includes theIPG 404. The lead system 508 may include electrodes 506. An example oflead system 508 includes one or more of the leads 408A-B. An example ofthe electrodes 506 includes at least a portion of the electrodes 406. Inthe illustrated example, the IPG 504 and the implantable lead system 508may provide DBS to a patient, with the neuromodulation target beingneuronal tissue in a subdivision of the thalamus of the patient's brain.Other examples of DBS targets include neuronal tissue of the globuspallidus (GPi), the subthalamic nucleus (STN), the pedunculopontinenucleus (PPN), substantia nigra pars reticulate (SNr), cortex, globuspallidus externus (GPe), medial forebrain bundle (MFB), periaquaductalgray (PAG), periventricular gray (PVG), habenula, subgenual cingulate,ventral intermediate nucleus (VIM), anterior nucleus (AN), other nucleiof the thalamus, zona incerta, ventral capsule, ventral striatum,nucleus accumbens, white matter tracts connecting these and otherstructures. The DBS targets may also include regions determinedanalytically based on side effects or benefits observed in one or morepatients, as well as regions specified by the user.

FIG. 6 illustrates, by way of example and not limitation, an embodimentof portions of a neuromodulation system 600. The system 600 includes anIPG 604, implantable neuromodulation leads 608A and 608B, an externalremote controller (RC) 624, a clinician's programmer (CP) 626, and anexternal trial modulator (ETM) 628. The system 600 may additionallyinclude external sensors configured to sense one or more physiologicalparameters, such as a heart rate sensor, a pulse oximeter, anelectrocardiogram sensor, an inertial sensor, or an electroencephalogramsensor, among others. The IPG 604 may be electrically coupled to theleads 608A and 608B directly or through percutaneous extension leads630. The ETM 634 may be electrically connectable to the leads 608A and608B via one or both of the percutaneous extension leads 630 and/or theexternal cable 632. The system 600 represents an embodiment of system100, with IPG 604 representing an embodiment of the neuromodulationdevice 104, electrodes 606 of leads 608A and 608B representing theelectrodes 106, and CP 626, RC 624, and the ETM 628 collectivelyrepresenting the programming device 102.

The ETM 628 may be standalone or incorporated into the CP 630. The ETM628 may have similar pulse generation circuitry as IPG 604 to deliverneuromodulation energy according to specified modulation parameters asdiscussed above. In an example, the ETM 628 is an external device andmay be used as a preliminary stimulator after leads 408A and 408B havebeen implanted and used prior to stimulation with IPG 604 to test thepatient's responsiveness to the stimulation that is to be provided byIPG 604. An external ETM 634 may be more easily configurable than theIPG 604.

The CP 626 may configure the neuromodulation provided by the ETM 628. Ifthe ETM 628 is not integrated into the CP 626, then the CP 626 maycommunicate with ETM 628 using a wired connection (e.g., over a USBlink) or by wireless telemetry such as using a wireless communicationslink. The CP 626 may also communicate with IPG 604 using a wirelesscommunications link 634.

An example of wireless telemetry is based on inductive coupling betweentwo closely-placed coils using the mutual inductance between thesecoils. This type of telemetry is referred to as inductive telemetry ornear-field telemetry because the coils must typically be closelysituated for obtaining inductively coupled communication. The IPG 604may include the first coil and a communication circuit. The CP 626 mayinclude or be otherwise electrically connected to the second coil suchas in the form of a wand that may be place near the IPG 604. Anotherexample of wireless telemetry includes a far-field telemetry link, alsoreferred to as a radio frequency (RF) telemetry link. A far-field, alsoreferred to as the Fraunhofer zone, refers to the zone in which acomponent of an electromagnetic field produced by the transmittingelectromagnetic radiation source decays substantially proportionally to1/r, where r is the distance between an observation point and theradiation source. Accordingly, far-field refers to the zone outside theboundary of r=λ/2π, where λ is the wavelength of the transmittedelectromagnetic energy. In one example, a communication range of an RFtelemetry link is at least six feet but may be as long as allowed by theparticular communication technology. RF antennas may be included, forexample, in the header of the IPG 604 and in the housing of the CP 630,eliminating the need for a wand or other means of inductive coupling. Anexample is such an RF telemetry link is a Bluetooth® wireless link.

The CP 626 may be used to set modulation parameters for theneuromodulation after the IPG 604 has been implanted. This allows theneuromodulation to be tuned if the requirements for the neuromodulationchange after implantation. The CP 626 may also upload information fromor download information to the IPG 604.

The RC 624 also communicates with the IPG 604 using a wireless link 636.The RC 624 may be a communication device used by the user or given tothe patient. The RC 624 may have reduced programming capability comparedto the CP 626. This allows the user or patient to alter theneuromodulation therapy but does not allow the patient full control overthe therapy. For example, the patient may be able to increase theamplitude of neuromodulation pulses or change the time that apreprogrammed stimulation pulse train is applied. The RC 624 may beprogrammed by the CP 626. The CP 626 may communicate with the RC 624using a wired or wireless communications link. In some embodiments, theCP 626 is able to program the RC 624 when remotely located from the RC624. In some examples, the RC 624 may download data to and upload datafrom the IPG 604.

FIG. 7 illustrates, by way of example and not limitation, an embodimentof implantable neuromodulation device 704 and one or more leads 708 ofan implantable neuromodulation system, such as the implantable system600. The implantable neuromodulation device 704 represents an embodimentof stimulation device 104 or 204 and may be implemented, for example, asthe IPG 604. Lead(s) 708 represents an embodiment of lead system 208 andmay be implemented, for example, as implantable leads 608A-B. Thelead(s) 708 includes electrodes 706, which represents an embodiment ofelectrodes 106 or 206 and may be implemented as electrodes 606. In someexamples, the implantable stimulator 704 may additionally becommunicatively coupled to one or more external sensors configured tosense one or more physiological parameters, such as a heart rate sensor,a pulse oximeter, an electrocardiogram sensor, an inertial sensor, or anelectroencephalogram sensor, among others.

The implantable neuromodulation device 704 may include a sensing circuit738 when the stimulator needs a sensing capability, neuromodulationoutput circuit 710, a neuromodulator control circuit 712, an implantstorage device 740, an implant telemetry circuit 742, a power source744, and one or more electrodes 714. The sensing circuit 738, whenincluded, may be configured to sense one or more physiologic signals forpurposes of patient monitoring and/or feedback control of theneuromodulation. Examples of the physiologic signals include neural andother signals each indicative of a condition of the patient that istreated by the neuromodulation and/or a response of the patient to thedelivery of the neuromodulation. The stimulation output circuit 212 iselectrically connected to electrodes 706 through one or more leads 708as well as electrodes 707, and delivers each of the neuromodulationpulses through a set of electrodes selected from electrodes 706 andelectrode(s) 707. The device control circuit 714 represents anembodiment of device control circuit 214, and controls the delivery ofthe pulses according to the stimulation configuration (includingstimulation parameters) received from the programming device 102 or 302.In one embodiment, the device control circuit 714 controls the deliveryof the pulses using the one or more sensed physiologic signals. Theimplant telemetry circuit 744 provides the implantable stimulator 704with wireless communication with another device, such as the CP 630 orthe RC 632, including receiving values of the plurality of stimulationparameters from the other device. The implant storage device 746 storesthe received stimulation configuration, including values of theplurality of stimulation parameters. The power source 748 provides theimplantable stimulator 704 with energy for its operation. The powersource 748 may include a battery. In one embodiment, the power source748 includes a rechargeable battery and a battery charging circuit forcharging the rechargeable battery. The implant telemetry circuit 744 mayalso function as a power receiver that receives power transmitted froman external device through an inductive couple. The electrode(s) 714allow for delivery of the pulses in the monopolar mode or unbalancedbipolar mode. Examples of the electrode(s) 714 include electrode 414Aand electrode 414B in IPG 404 as illustrated in FIG. 4A.

In an example, the implantable neuromodulation device 704 may be used asa master database. A patient implanted with implantable stimulator 704(such as may be implemented as IPG 604) may therefore carry patientinformation needed for his or her medical care when such information isotherwise unavailable. The implant storage device 740 may be configuredto store such patient information. For example, the patient may be givena new RC 632 and/or travel to a new clinic where a new CP 630 is used tocommunicate with the device implanted in him or her. The new RC 632and/or CP 630 may communicate with the implantable stimulator 704 toretrieve the patient information stored in implant storage device 740through the implant telemetry circuit 744 and the wireless communicationlink 640, and allow for any necessary adjustment of the operation of theimplantable stimulator 704 based on the retrieved patient information.The patient information be stored in the implant storage device 746 mayinclude, for example, various types of neuromodulation settings.Examples may include positions of lead(s) 708 and electrodes 706relative to the patient's anatomy (transformation for fusingcomputerized tomogram (CT) of post-operative lead placement to magneticresonance imaging (MRI) of the brain), clinical effect data, objectivemeasurements using quantitative assessments of symptoms (e.g., usingmicro-electrode recording, accelerometers, and/or other sensors), and/orother information considered important or useful for providing adequatecare for the patient. In various examples, the patient information to bestored in implant storage device 740 may include data transmitted toimplantable stimulator 704 for storage as part of the patientinformation and data acquired by implantable stimulator 704, such as byusing sensing circuit 742.

FIG. 8 is a schematic diagram illustrating radial current steering alongvarious electrode levels along the length of the lead 808. The segmentedelectrode configuration is capable of steering current in the x-axis,y-axis, and z-axis. Radial coordinates may be used including L (aposition along the lead axis), r (a distance from the lead axis) andangle θ. Thus, the centroid of stimulation may be steered in anydirection in the three-dimensional space surrounding the lead. Thestimulation may be shifted at each level along the length L of the lead.The use of multiple sets of segmented electrodes at different levelsalong the length of the lead allows for three-dimensional currentsteering. In some embodiments, the sets of segmented electrodes areshifted collectively, and in at least some other embodiments, each setof segmented electrodes is controlled independently. Each set ofsegmented electrodes may contain two, three, four, five, six, seven,eight or more segmented electrodes. It will be understood that differentstimulation profiles may be produced by varying the number of segmentedelectrodes at each level.

FIGS. 9A-9E illustrate, by way of example, and not limitation, variousexamples of leads with which radial current steering may he used. Thefigures illustrate lead bodies 846, segmented electrodes 848 (e.g. 3electrodes spaced 120°from each other around the lead.) ring electrodes850, and a tip electrode 852. These figures are not intended to limitthe present subject matter to any particular arrangement of electrodes,but are intended to illustrate that these electrode arrangements arecapable of having the current distributed over active ones of theseelectrode to control the shape, size and direction of the modulationfield. The current delivered to each of the active electrodes can beindependently controlled to provide a very large number of parametersetting options to create modulation fields of many shapes and sizes.Polarity is one of the parameter setting options.

FIG. 10 illustrates examples of fractionalizations to provide differentmodulation types. The neuromodulation device includes an IPG with a canelectrode, and a lead with 8 sets or rows of electrodes, where each setor row includes 3 fractionalized electrodes distributed around the lead(e.g. at 0°, 120° and 240). The figure illustrates six tables that have8 rows and 3 columns. Each table represents the lead electrodes, suchthat each cell in the table represents one of the electrodes on lead,and the cell below the table represents a can electrode. The data withinthe cells is intended to illustrate fractionalization values for theelectrodes. The total positive charge (anodic) will equal the totalnegative charge (cathodic) in the system. However, up to 100% of theanodic or cathodic contribution may be provided by the can electrode. Assuch, the polarity of the modulation field at the targeted region(s) maybe 100% cathodic (conventional monopolar DBS neuromodulation), 100%anodic (anodic monopolar), mostly cathodic (e.g. cathodic major), mostlyanodic (anodic major), or balanced such as may occur if there is nocurrent contribution by the can or other reference electrode). It may bepossible to provide balanced polarity in so far as the can electrode isnot providing a current contribution, but the current contributions forone polarity is spread across many electrodes and the currentcontributions for the other polarity is provided by one or a fewelectrodes. Thus, it may be possible to provide a balanced modulationusing the lead electrodes, but still provide an anodic effect for aparticular targeted region.

The polarity of the modulation field at the lead electrodes (illustratedby the values within the table cells) may be 100% anodic (no cathodiccontribution), 100% cathodic (no anodic contribution), balanced (anodiccontribution equals cathodic contribution), approximately or nearlybalanced (anodic contribution is within +/−X % of cathodic contribution;such as 45%:50% or 50%:45% if X=5). To be characterized as relativelybalanced, a relatively small percentage (X % where X=10 or less by wayof example) of the cathodic or anodic contribution may be provided bythe can electrode. As identifier earlier, the polarity of the field mayalso be considered substantially monopolar or pseudo-monopolar if thecan is contributing a relatively larger percentage of the current (e.g.90% or higher). The present subject matter is not limited to thesevalues, as the system is capable of using the lead electrodes to provideanywhere from 0 to 100% of the total anodic energy, or using the leadelectrodes to provide anywhere from 0 to 100% of the total cathodicenergy. If something is anodic major, the anodic contribution of thelead electrodes sums to 100%, but the cathodic contribution of the leadelectrodes sums to less than 100%. Similarly, cathodic major indicatesthat the cathodic contribution of the lead electrodes sums to 100%, butthe anodic contribution of the lead electrodes sums to less than 100%.

These fractionalizations and stimulation type may be stored asneuromodulation settings for a programmed neuromodulation therapy. Otherinformation that may be stored as neuromodulation parameter settings mayinclude clinical effects, targeted region(s), and avoidance region(s),if any, to particular neuromodulation parameter settings (e.g.amplitude, pulse width, fractionalization, polarity).

FIG. 11 illustrates an example of a presentation of clinical effects andstimulation configuration on a user interface. The target volume and theone or more clinical effects may be presented, along with a stimulationconfiguration (e.g. polarity and fractionalization, and stimulationpulse parameters including amplitude, width, and frequency). A“Navigator” button can open a navigation console that allows the user tonavigate predefined stimulation configurations and select a predefinedstimulation configuration. A “Manual” button can open a manualprogramming console that allows the user to manually define astimulation configuration.

After the target volume is determined, stimulation configurationcircuitry 962 can automatically generate the stimulation configurationfor activating a volume of tissue substantially matching the targetvolume. An inverse modeling algorithm may be used to automaticallygenerate the stimulation configuration for activating a volume of tissuein the patient that substantially matches the target volume. The targetvolume can be defined and refined by one or more iterations using theone or more clinical effects resulting from the test volume used in eachiteration. For example, a test volume may be generated, where the testvolume would from delivery of neuromodulation using the stimulationconfiguration. Alternatively, a test volume can be specified, and theinverse modeling algorithm can be used to automatically generate thestimulation configuration for activating that test volume. In oneembodiment, the inverse modeling algorithm relates a stimulationconfiguration to a volume of activation (“VOA”). VOA designates anestimated region of tissue that will be stimulated for a particular setof stimulation parameters. The terms “stimulation field map” (SFM) alsorefer to the VOA This VOA may be referred to as a stimulation fieldmodel (SFM). Thus, for a given polarity, pulse width, frequency,fractionalization, the field and tissue modeling information candetermine the current required to activate a volume of tissue (e.g.threshold current). This information may be used to create a SFM. Thestimulation configuration can be generated using a library includingdata mapping volumes of activation to stimulation configurations and/orusing an analytical derivation of the stimulation configuration thatgenerates the stimulation volume.

In various embodiments, the system can determine the one or moreclinical effects and/or present the one or more clinical effects usinginformation entered by the user, information entered by the patient,and/or signals sensed from the patient. The clinical effects can includethose represented by one or more types of therapeutic benefits and oneor more types of side effects. A therapeutic benefit scorerepresentative of a degree of the one or more therapeutic benefits (0for no therapeutic benefit, 4 for highest degree of therapeuticbenefit), and a side effect score representative of a degree of the oneor more side effects (0 for no side effect, 4 for highest degree of sideeffect), may be presented. The clinical effects may be presented as atherapeutic benefits contour which is indicative of a volume of thetissue excitable for one or more desirable therapeutic benefits, and aside effect contour which is indicative of a volume of the tissueexcitable for one or more unwanted side effects.

To estimate or determine a SFM, the electric field arising from theelectrical energy delivered according to the stimulation parameters maybe determined or modeled, and then the tissue response to an electricalfield may be determined or modeled. The modeling for the electric fieldand the tissue response can be used to estimate or determine the SFM.Electric fields may be modeled in a variety of ways, including but notlimited to a finite element analysis model. Tissue responses may also bemodeled in a variety of ways, including but not limited to a neuralelement model or axon model or some estimator that calculates featuresof the electric field and uses them to determine the current thresholdrequired to induce a response.

FIG. 12 illustrates a series of planes distributed around a lead havingelectrodes and threshold tables for those planes. The information (e.g.SFM) based on the electric field model and tissue response model can beused to produce planar distributions of stimulation threshold values forthese planes. These stimulation threshold values may be dependent onother stimulation parameters, such as stimulation duration (for example,pulse width), stimulation frequency, and the like. Each of the planescan be divided into multiple regions (for example, squares orrectangles) with an associated stimulation threshold value (such as athreshold current or voltage) which, when applied to the lead willactivate or stimulate the tissue at that region, as illustrated in FIG.12. By way of example, the stimulation threshold value may be athreshold current (I_(th)). However, a threshold voltage or otherelectrical characteristic may be used to identify the stimulationthreshold value. The radial coordinates x, z and θ may be used touniquely identify a particular region of a particular plane. The x-valuecorresponds to a radial distance from the lead. The z-value correspondsto an axial coordinate along the longitudinal axis of the lead. Theθ-value corresponds to the relative angle of the plane in which theregion resides. Thus, the I_(th) values can be stored in a database as aseries of I_(th) tables, I_(th)(z, x, θ), which can also be indexedrelative to other state variables, as described below. A visual exampleof these I_(th) tables is presented in FIG. 12 where each plane(identifiable by θ) represents one of the tables. The illustrated I_(th)tables are simple tables. As the characterized regions on each of theplanes gets smaller, the number of I_(th) values gets larger. Also, asthe threshold current is affected by polarity, pulse width, frequency,fractionalization and other pulse parameters, the data elements in thedatabase of Ith values may be specific to the various combinations ofpolarity, pulse width, frequency, fractionalization and the other pulseparameters.

The neuromodulation programming may involve steering the field throughspace around the lead. An example of a steeling parameter is “rotation”which represents the angular direction of the field extending away fromthe lead. Another example of a steering parameter is “spread” whichrelates to the angular spread of the field around the circumference ofthe lead. In addition to rotation and spread, the stimulation (e.g.,stimulation current) can be steered to different positions andarrangements around the lead using “axial position” in z direction. Thesystem may be configured to convert these steering parameters (z,spread, rotation) into current fractionalization parameters for theelectrodes on the lead.

The large database of I_(th) values can be compressed using a variety oflossless or lossy techniques. Examples of lossless compression involvesthe recognition that many fractionalization states are not unique oravailable, and the recognition of symmetry and redundancy in the data.The database can be reduced to a set of unique I_(th) tables and a mapwhich relates the Ith tables to the different fractionalizations (i.e.,the different axial position, rotation, and spread values) and,optionally, to different pulse widths or frequencies. Lossy compressionmay approximate Ith tables that are determined to be sufficientlysimilar to the original table. Another lossy compression method may useMPEG compression or a similar process that generates data describingdifferences in the data.

The regions of space for which I_(th) values are determined may berepresented using “voxels”. A voxel represents a volumetric element of acomputerized physiologic structure or analytically determined structure,such as a computerized tissue representation, in a 3D space. A voxel mayhave specified size in each dimension, such as 0.5 mm or less. The 3Dvoxelized model may include a computer-generated graphic modelrepresenting volumetric tissue elements and their responses to theelectrostimulation. In an example, the 3D voxelized model comprises anarray of 3D voxels each specified as belonging to one of a plurality ofphysiologic structures, such as a target region or an avoidance region.A target region may refer to a physiologic structure, analyticallyderived or user selected regions (e.g., of the brain or other areascombinations thereof. The target region may be associated with knowntherapeutic benefits of the electrostimulation. An avoidance region mayrefer to a physiologic structure, analytically derived or user selectedregions, or combinations thereof that are associated with a known sideeffect of the electrostimulation. Each target or avoidance region may beassigned a corresponding weight factor w correlated to a clinicaloutcome of electrostimulation delivered at respective physiologicstructures, such as a therapeutic benefit or a side effect. For example,a target region (S⁺) may be associated with a positive weight factor,and an avoidance region (S⁻) may be associated with a negative weightfactor. The absolute value of the weight factor signifies relativesignificance of the clinical outcome. In an example of DBS, an avoidancestructure that produces a slight slur (a side effect of DBS) may be ofless clinical significance or importance than an avoidance structurethat causes seizure (another side effect of DBS). As such, a weightfactor with a larger absolute value may be assigned to theseizure-causing structure. A user may assign or adjust weight factorsfor various regions based on known clinical effects of theelectrostimulation on the respective regions.

Each of the 3D voxels of the received 3D voxelized model has a voxelvolume and a voxel value. The voxel volume represents a geometric sizeof the voxel. The voxel value may, among other possibilities, representa likelihood that the corresponding voxel volume may contribute to theclinical outcome (therapeutic benefit or side effect). For example, avoxel value of 0.8 indicates that the voxel has 80% chance of having thebenefit or side effect of the structure to which it is a part. Inanother example, multiple regions could be represented in the samestructure of voxels, where the voxel values represent the relativeweights of the various regions (e.g., the slur versus the seizureinducing regions). Each set of voxels may either belong to the targetregions (with therapeutically beneficial effects) or avoidance regions(with side effects). In yet another example, all regions, including boththe target and avoidance regions, may be represented in the same set ofvoxels, in which case all the voxels in the avoidance region areassigned negative voxel values, and all the target voxels are assignedpositive voxel values. In an example, a user may adjust one or more ofthe weight factors w associated with various target structures andavoidance structures, the voxel volumes, or the voxel values associatedwith the 3D voxels.

The 3D voxelized model, along with the voxel volumes and the voxelvalues associated with the 3D voxels, may be used to determine a metricvalue. A preprocessing step may translate the target and side effectregions, which are in “patient space” into “lead space”. The patientspace may be coordinate system based on the imagery of the patientshead. Lead space may be a coordinate system that has (x=0, y=0; z=0) atthe center of the lead at the distal edge of the distal row ofelectrodes. Increasing z is up the lead towards the proximal end. The xaxis goes through the center of electrode 2 (for all lead types), andthe y axis is the cross product of the z and x axes. The system maydetermine, for each of the regions, a respective metric value (MV) usingthe received 3D voxelized model. The MV represents a clinical effect ofelectrostimulation on the tissue according to a stimulation current andfractionalization of electrical current. In an example, the MV may becomputed using a weighted combination of the volumes of the array of 3Dvoxels in the voxelized model. For example, for each 3D voxel i in aregion j (either a target region or an avoidance region), acorresponding voxel effect (X(i, j)) representing voxel i's contributionto the MV for the region j, may be computed using the followingequation:

X(i,j)=vol(j)*w(j)*val(i,j)  (1)

where vol(j) represents the voxel volume for each voxel in region j, andw(j) represents the weight factor associated with the region to whichthe voxel i belongs (region j in this case), and val(i, j) representsthe voxel value for voxel i in region j. All the voxels in a particularregion (e.g., region j) have the same voxel volume (vol(j)), and all thevoxels in a particular region (e.g., region j) share the same weight(w(j)) of that region. When there is one 3D matrix of voxels thatcontains both target and side effect voxels, the voxel-specific weightfactor w(i, j) is a positive scalar if the 3D voxel i is situated in atarget region, or a negative scale if the 3D voxel i is situated in anavoidance region. In the general case, the voxel values are alwayspositive, and the weight of the region is positive for target regionsand negative for side effect regions Therefore, the 3D voxels situatedin a target region (S⁺) have positive voxel effects X, and the 3D voxelssituated in an avoidance region (S⁻) have negative voxel effects X. Theabsolute value of the weight factor represents relative significance ofthe clinical outcome. In an example, the weight factor is a non-zeroscalar between −1 and 1. In general, the target region has a weight of 1and the side effect regions have any negative value. The magnitude ofthe side effect regions weight gives the relative importance assigned tothe side effect. A range for side effect weights may be 0>weight>=−100.If the weight is zero, it has no importance. If the weight is −100, thatmeans stimulating a voxel volume of it can wipe out the benefit of 100times that volume of target voxels The MV for the region j may bedetermined using a combination of voxel effects across all N voxelswithin the region j in the 3D voxelized model that have been stimulated,according to the following Equation:

MV=X(1,j)+X(2,j)+ . . . +X(N,j)  (2)

Associated with the MV is a stimulation current (Is) applied to the 3Dvoxels in the region. The MV may also be associated with electricalcurrent fractionalization (F_(I)) across a plurality of electrodes thatdeliver stimulation energy to the region. The relationship among I_(S),F_(I), and the MV may be represented as: I_(S)=f(MV, F_(I)), where f isa linear or nonlinear function. The MV represents a clinical outcomewith tissue recruitment from a physiologic or analytically determinedregion. The tissue recruitment may be represented by the number of 3Dvoxels recruited and their respective voxel effect X(i,j).

The system may determine a stimulation configuration corresponding to abest metric value (MV_(opt)) that satisfies a specific optimizationcondition. In an example, MV_(opt) may be identified as a metric valuethat exceeds a threshold metric value MV_(TH). In another example,MV_(opt) may be identified as the largest MV under a specific electricalcurrent fractionalization F_(I). According to the relationship (Is,MV)=function of (Fi, PW, X), where X is the overall list of voxel metriceffects, or X=function of (Target & weight, Avoidance regions &weights). The fractionalization and pulse width determine the Ith tableto be used. The target and weight and side effects and weights determinethe effect each voxel has on the metric. There may be an avoidancevolume that includes all of the volume of the patient's head and hassome usually much lower weight. The purpose of this avoidance volume isto keep the amplitude as low as is feasible, as any increase inamplitude adds the additional cost of that volume of tissue. Combiningthe Ith and the metric contribution of each voxels gives a metric effectof each change in amplitude, Summing along the list of metric changes byamplitude gives the amplitude that produces the best metric value. Theremay be more than one amplitude that produce the best metric value, andthe lowest amplitude with the metric value is used. The inputs mayinclude the fractionalization and pulse width, and the target andavoidance regions and their weights. The output may include the bestmetric value and the amplitude that had the best metric value. Thesystem may iterate over fractionalizations to find the one with bestmetric value. That fractionalization and the lowest amplitude associatedwith that metric value are returned as discussed above, associated withthe MV_(opt) (e.g., a maximum MV under the specific electrical currentfractionalization F_(I)) is a corresponding Is. In an example, thestimulation configuration generator circuit 824 may determine a minimalstimulation current (I_(min)) that results in an MV_(opt). The MV_(opt)may be associated with a specific current fractionalization F_(I)*. Therelationship among I_(min), F_(I)*, and MV_(opt) may be represented as:I_(min)=f(MV_(opt), F_(I)*). The minimal stimulation current I_(min) andthe current fractionalization F_(I)* that correspond the best metricvalue MV_(opt) may be programmed into the neuromodulator to generate anddeliver electrostimulation to the tissue.

Additional information regarding clinical effects may be found in U.S.patent application Ser. No. 15/902,163, filed Feb. 22, 2018, andentitled “Method and Apparatus For Clinical Effects-Based Targeting ofNeurostimulation”; and U.S. Provisional Patent Application No.62/598,558, filed Dec. 14, 2017, and entitled “Systems and Methods forClinical Effect-Based Neurostimulation”. U.S. patent application Ser.No. 15/902,163 and U.S. Provisional Patent Application No. 62/598,558are hereby incorporated by reference in their entirety.

FIG. 13 illustrates an embodiment of a system for adjustingneuromodulation parameters used by a neuromodulator 1304 operablyconnected to a plurality of electrodes to modulate a neural target. Thesystem has a translator 1354 for translating a first set ofneuromodulation parameters specific for one type of neuromodulationpolarity to a second set of neuromodulation parameters specific toanother type of neuromodulation polarity. Thus, the translator 1354accounts for the different reactions (e.g. thresholds, clinical effects,and the like) that different types of neural structures may have todifferent neuromodulation polarities. In some embodiments, thetranslator 1354 may reside in a clinician programmer 1302 or otherdevice. In some embodiments, the translator 1354 may reside locally orremotely within a system networked with the clinician programmer 1302 orother device. Once the neuromodulation parameters have been translated,the system may keep the original set of neuromodulation parameters andthe translated set of parameters (such as in different memory locations)to enable the system to deliver both types of neuromodulation, or thesystem may replace the original set of neuromodulation parameters withthe translated set of neuromodulation parameters.

FIG. 14 illustrates functions of a translator such as may be implementedin the system of FIG. 13, Generally, the translator 1454 has access toat least some first parameter settings for a first polarity type, and isconfigured to generate second parameter settings for a differentpolarity type of neuromodulation. Examples of such polarity typesinclude cathodic monopolar, anodic monopolar, cathodic major, anodicmajor, and balanced.

Some translator embodiments use only the pulse parameter data 1456 togenerate the second parameter settings. Some translator embodimentsaccess related data such as programming data 1458 (e.g. I_(th) table,SFM, model information and the like) used to program the first pulseparameter data, and use that programming data to generate the secondparameter settings. Some translator embodiments access related data suchas programming settings that include anatomical regions 1460 (e.g.target, avoidance, weight, and the like) used to program the first pulseparameter data, and use those programming settings that includeanatomical regions to generate the second parameter settings. Theseembodiments are described in more detail below.

Some translator embodiments translate the stimulation polarity of aprogram setting by inverting the polarity of any electrode that is in acathodic or anodic state without changing the fractionalization oramplitude. Some translator embodiments may further multiply theamplitude of the first parameter settings by a scale factor to providethe amplitude of the second parameter settings, again without changingthe fractionalization. The scale factor may depend on the polarity typeof neuromodulation provided by the first parameter settings (e.g.whether the original fractionalization is mainly cathodic, mainly anodicor balanced). For example, the scale factor may be 1.5 if originallycathodic to accommodate higher energy needs of anodic energy to activateneural tissue, may be 0.66 if originally anodic to accommodate lowerenergy needs of cathodic energy activate neural tissue, or may be 1.0 iforiginally balanced. Some embodiments may further base the scale factorusing one or more of: the polarity within the first parameter settings;the amplitude within the first parameter settings; a pulse width withinthe first parameter settings; a waveform within the first parametersettings; a frequency within the first parameter settings; at least oneburst-related parameter within the first parameter settings; or one ormore locations of modulation within the first parameter settings. Anequation may be used having weighted values for these settings such thatthe scale factor is dependent on those settings. Some embodiments maydetermine the scale factor using a ratio between a first therapeuticrange for the first parameter settings and a second therapeutic rangefor the second parameter settings. The first therapeutic rangerepresents a range of amplitudes for the first parameter settings thatextends from a therapeutic threshold to a side effect threshold. Thesecond therapeutic range represents a range of amplitudes for the secondparameter settings that extends from a therapeutic threshold to a sideeffect threshold. Some embodiment may determine a look-up table is usedto determine the scale factor, and at least one of the polarity, thepulse width, the amplitude, the waveform, the frequency, the at leastone burst-related parameter, or the one or more locations of modulationis used to index into the look-up table, the scale factor being anearest value or an interpolated value.

For all cases, the scale factor may be determined and applied on anelectrode by electrode basis. All the anode electrodes are turned tocathodes and their amplitude is multiplied by 0.67. All the cathodeelectrodes are turned into anodes and their amplitudes multiplied by 1.5

Rather than multiplying the amplitude by a scale factor, someembodiments use I_(th) tables to determine the amplitude for the secondparameter settings. For example, the amplitude for the second parametersmay be determined by generating an I_(th) table for thefractionalization in the first parameter settings and determining themaximum radius r of the SFM at the original amplitude. Then an I_(th)table can be generated for the translated fractionalization (where thepolarity of the electrical contribution for each active electrode isinverted), and finding the minimum I_(th) value at the radius r, andusing the amplitude as the amplitude for the second parameter settings.

Rather than multiplying the amplitude by a scale factor, someembodiments construct a first I_(th) table characterizing spatial pointsfor the fractionalization values in the first parameter settings,wherein the first parameter settings provide a first SFM, determine avolume of a SFM at the amplitude in the first parameter settings,construct a second I_(th) table characterizing the spatial points forthe second parameter settings to provide a second SFM, determine anamplitude that provides the second SFM with a volume that equals orapproximately equals the first SFM, and use the determined amplitude inthe second parameter settings.

Some translator embodiments invert the polarity of the original settingsthat may change the fractionalization of the current among theelectrodes in the second parameter settings. For example, the system mayhave the ability to receive, via a user interface, at least one targetregion to be targeted using a neuromodulation field and zero or moreavoidance regions to be avoided using the neuromodulation field, anddetermine fractionalization values for the second parameter settings tomodulate the at least one target region and avoid the zero or moreavoidance regions. The system may receive a polarity input indicatingwhether to provide anodic neuromodulation, cathodic neuromodulation orbalanced or approximately balanced neuromodulation. The controller isconfigured to control a polarity of neuromodulation provided by theneuromodulator according to the received polarity input.

Some embodiments take the program settings for the first parametersettings, and return a new program inverts the polarity and that changesthe fractionalization and amplitude, such that the fractionalization andamplitude in the second parameter settings are different than thefractionalization and amplitude in the first parameter settings. Someembodiments may determine a target from the SFM of the first parametersettings, and then find the matching fractionalization and amplitude forthe different polarity type (e.g. inverted polarity). Some embodimentscreate an I_(th) table for the original fractionalization, determine aninitial fractionalization based on the original fractionalization butwith the opposite polarity, determine the I_(th) table for the initialfractionalization, determine the scaling factor that produces theminimum sum of the least squares difference between the original I_(th)table and the I_(th) table produced by the initial fractionalization,store the fractionalization, the scaling factor and the sum of the leastsquared differences, and optimize via an iterative process until stopcriteria is achieved, the initial second fractionalization values into asecond fractionalization values in the second table that has a least sumof the squared difference between the first table and the scaled secondtable. This step involves iteratively: (1) determining a differentsecond fractionalization, (2) generating the Ith table for new secondfractionalization, (3) calculating the scaling factor that produces theleast squared differences, and (4) determine if stopping criteria havebeen met and either returning the best answer or going back to step 1.As the I_(th) tables include a large number of I_(th) values, variousembodiments may only calculate the sum of the least squared differencesfor the part of the original I_(th) table with values below a limitingvalue. Some embodiments calculate the sum of the least squareddifferences for the part of the newly calculated I_(th) table withvalues below a limiting value. Some embodiments calculate the sum of theleast squared differences for the part of where both the original I_(th)table and newly calculated I_(th) table with values below a limitingvalue.

The limiting value may be entered by a user, or may be calculated by thesystem. The limiting value may be calculated from the amplitude in thefirst parameter settings. Example of rules include 1.5 times theoriginal amplitude, the original amplitude squared if the originalamplitude is greater than 1, or the square root of the originalamplitude if the original amplitude is less than or equal to 1, or someother rule set. The limiting value may be calculated using the maximumvalue in the original Ith table, e.g. 0.5 times the maximum Ith value.The limiting value may be calculated using the maximum value of theoriginal Ith table and the original amplitude, e.g. half way between theoriginal amplitude and the maximum value. The limiting value may becalculated using the maximum value in the newly calculated Ith table,e.g. 0.5 times the maximum Ith value. The limiting value may becalculated using the maximum value of the newly calculated Ith table andthe original amplitude, e.g. half way between the original amplitude andthe maximum value. The limiting value may be calculated using themaximum value of the original Ith table, the maximum value of the newlycalculated Ith table and the original amplitude, e.g. half way betweenthe original amplitude and the minimum of the two maximum values.

Some embodiments may use a fractionalization look up table based on avirtual electrode that produces multi polar fractionalizations. Avirtual electrode may be used to index into the fractionalizationlook-up table to produce multipolar fractionalizations. A virtualelectrode is a method of converting steering coordinates intofractionalization. Each virtual electrode has a look up table for eachlead type, so the lead type and virtual electrode type dictate whichlook up table to use. That look up table contains a sub-table for eachelectrode for that lead type. The axial and rotational coordinates areused as the index values into the sub-tables. The original table is anunbalance multi polar table. This table can be used as either CathodicMajor or Anodic Major by changing the sign of the fractionalizationvalue on each electrode. Other tables could be created that matches thecurrent monopolar steering states, where there would be a virtualelectrode for each spread state. Through a user interface, a user maysteer the virtual electrode according to the one or more virtualelectrode steering parameters. The system may determine electricalcurrent fractionalization across a plurality of electrodes based on thevoltage field of the virtual electrode. Some embodiments use afractionalization look up table that produces monopolarfractionalizations. Steering coordinates, including z axis, rotation,and spread coordinates, may be used to index into the fractionalizationlook-up table to produce monopolar fractionalizations.

Some translator embodiments use target and avoidance regions, known bythe system and used to create the first parameter settings, to generatethe second parameter settings. The system settings for the firstparameter settings may include a target and 0 or more avoidance regions,where each region has an associated weight, which may be shared betweenregions, a background weight, possibly voxelization settings, andpossibly a stimulation type.

The first parameter set may not include a known “stimulation type.” Someembodiments use the known anatomical regions (e.g. target andavoidance), set a polarity type (e.g. Anodic major) for neuromodulation,and then determine the fractionalization for the set polarity type tomodulate the targeted region and avoid any avoidance regions.

Some system embodiments may identify the target and avoidance regions asbeing only applicable to particular polarity types. For example, oneregion may need to be avoided for cathodic major neuromodulation, butmay not need to be avoided for anodic major neuromodulation. Thesetarget/avoidance regions may be considered polarity-specific regionsthat are specific to one or more of cathodic major neuromodulation,anodic major neuromodulation, or balanced or relatively balancedneuromodulation.

Target or avoidance regions that are applicable to a subset of thestimulation types may be associated with similar effect regions that areapplicable to another, possibly overlapping, subset of the stimulationtypes. The associated regions are presented to the user as a singleoption (e.g. “Rigidity”: associated regions that have the clinicaleffect on rigidity). Some embodiments of the system only use the targetand avoidance regions that are applicable to polarity type ofneuromodulation. Some embodiments may take an original set ofprogramming settings, inverts the commanded stimulation type, anddetermines the fractionalization.

Various embodiment use fractionalization look-up tables that arespecific to the neuromodulation. For example, some embodiments use aone-polarity major multipolar fractionalization look-up table to produceanodic major multipolar fractionalization or cathodic major multipolarfractionalization. Some embodiments a monopolar look-up table to produceanodic monopolar or cathodic monopolar fractionalizations. Someembodiments a monopolar look-up table to produce polarity-balancedfractionalizations. Producing balanced multipolar fractionalizationsuses a lookup table created in a balanced manner. Similarly, there maybe a monopolar table (same table can be used for anodic or cathodicmonopolar) and a multipolar unbalanced (same table for anodic major orcathodic major). This table may have the preferred polarity at thetarget location, with the opposite polarity spread across the remainingelectrodes in some manner. As with the other tables, the output would beeither cathodic balanced or anodic balanced, by inverting thefractionalization values for each electrode.

Some of the previously-described translator embodiments may use languagespecific to DBS programming. However, the present subject matter is notlimited to such DBS programming. By way of example, the followingnonlimiting embodiments may be used to translate neuromodulationpolarities for SCS therapies. For example, some SCS embodiments receivean input indicating whether to provide anodic neuromodulation, cathodicneuromodulation or balanced or approximately balanced neuromodulation,and determine electrode fractionalizations for the plurality ofelectrodes to provide at least one target pole for a neuromodulationfield based on the neural target and the received input. The electrodefractionalizations for anodic neuromodulation may be based on an anodicstimulation field model representative of anodic activation threshold,the electrode fractionalizations for cathodic neuromodulation may bebased on a cathodic stimulation field model representative of cathodicactivation threshold, and the electrode fractionalizations for balancedor approximately balanced neuromodulation may be based on a balancedstimulation field model representative of balanced activation threshold.Some SCS embodiments may automatically translate the first parametersettings into the second parameter settings by changing the polarity forthe at least one target pole, and changing the fractionalization and theamplitude. Some SCS embodiments may automatically translate the firstparameter settings into the second parameter settings by determining atarget pole for a first neuromodulation field that corresponds to thefirst parameter settings, and determining electrode fractionalizationsand amplitude for the second parameter settings to provide the targetpole for a second neuromodulation field. The target pole for the secondneuromodulation field may have an inverted polarity from the at leastone target pole for the first neuromodulation field. In some SCSembodiments, the parameters are automatically translated by constructinga first table for first fractionalization values in the first parametersettings to characterize spatial points, determining initial secondfractionalization values for the second parameter settings based on thefirst fractionalization values wherein elements of the initial secondfractionalization values have an opposite polarity with respect toelements of the first fractionalization values, constructing a secondtable using the initial second fractionalization values, determining ascaling factor for the initial fractionalization values in the secondtable that produces a minimum sum of the squares difference between thefirst table and the scaled second table, and optimizing the initialfractionalization values into a second fractionalization values in thesecond table that has a least sum of the squared difference between thefirst table and the scaled second table. Some SCS embodiments determineelectrode fractionalizations using a fractionalization look-up table fora target multipole or using a fractionalization look-up table for atarget monopole. Some embodiments automatically translate the firstparameter settings into the second parameter settings by setting theneuromodulation polarity type to anodic major, and determiningfractionalization values for the second parameter settings to modulatethe neural target using anodic major neuromodulation.

FIG. 15 is a block diagram illustrating a machine in the example form ofa computer system, within which a set or sequence of instructions may beexecuted to cause the machine to perform any one of the methodologiesdiscussed herein, according to an example embodiment. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of either a serveror a client machine in server-client network environments, or it may actas a peer machine in peer-to-peer (or distributed) network environments.The machine may be a personal computer (PC), a tablet PC, a hybridtablet, a personal digital assistant (PDA), a mobile telephone, animplantable pulse generator (IPG), an external remote control (RC), aUser's Programmer (CP), or any machine capable of executing instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.Similarly, the term “processor-based system” shall be taken to includeany set of one or more machines that are controlled by or operated byone or more processors (e.g., a computer) to individually or jointlyexecute instructions to perform any one or more of the methodologiesdiscussed herein.

An example of a computer system includes at least one processor (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) or both,processor cores, compute nodes, etc.), a main memory and a staticmemory, which communicate with each other via a link (e.g., bus). Thecomputer system may further include a video display unit, analphanumeric input device (e.g., a keyboard), and a user interface (UI)navigation device (e.g., a mouse). In one embodiment, the video displayunit, input device and UI navigation device are incorporated into atouch screen display. The computer system may additionally include astorage device (e.g., a drive unit), a signal generation device (e.g., aspeaker), a network interface device, and one or more sensors (notshown), such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. It will be understood that other formsof machines or apparatuses (such as IPG, RC, CP devices, and the like)that are capable of implementing the methodologies discussed in thisdisclosure may not incorporate or utilize every component depicted inFIG. 15 (such as a GPU, video display unit, keyboard, etc.).

The storage device includes a machine-readable medium on which is storedone or more sets of data structures and instructions (e.g., software)embodying or utilized by any one or more of the methodologies orfunctions described herein. The instructions may also reside, completelyor at least partially, within the main memory, static memory, and/orwithin the processor during execution thereof by the computer system,with the main memory, static memory, and the processor also constitutingmachine-readable media.

While the machine-readable medium is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions. The term “machine-readable medium” shallalso be taken to include any tangible (e.g., non-transitory) medium thatis capable of storing, encoding or carrying instructions for executionby the machine and that cause the machine to perform any one or more ofthe methodologies of the present disclosure or that is capable ofstoring, encoding or carrying data structures utilized by or associatedwith such instructions. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media. Specific examples ofmachine-readable media include non-volatile memory, including but notlimited to, by way of example, semiconductor memory devices (e.g.,electrically programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions may further be transmitted or received over acommunications network using a transmission medium via the networkinterface device utilizing any one of a number of well-known transferprotocols (e.g., HTTP). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), the Internet,mobile telephone networks, plain old telephone (POTS) networks, andwireless data networks (e.g., Wi-Fi, 3G, and 4G LTE/LTE-A or 5Gnetworks). The term “transmission medium” shall be taken to include anyintangible medium that is capable of storing, encoding, or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such software.

The above detailed description is intended to be illustrative, and notrestrictive. The scope of the disclosure should, therefore, bedetermined with references to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method for adjusting neuromodulation parametersused by a neuromodulator operably connected to a plurality of electrodesto modulate a neural target, comprising: determining that a translationtrigger has occurred; and in response to determining that thetranslation trigger has occurred, automatically translating firstparameter settings into second parameter settings, wherein theautomatically translating the first parameter settings into the secondparameter settings includes either automatically translating fromcathodic parameter settings to anodic parameter settings, orautomatically translating from anodic parameter settings to cathodicparameter settings, wherein the neuromodulator is configured to usecathodic parameter settings for the neuromodulation parameters todeliver cathodic modulation to the neural target, and is configured touse anodic parameter settings for the neuromodulation parameters todeliver anodic modulation to the neural target.
 2. The method of claim1, wherein the first parameter settings for the neuromodulationparameters include at least a polarity, an amplitude, and afractionalization, the automatically translating the first parametersettings into the second parameter settings includes changing thepolarity for each active electrode.
 3. The method of claim 2, whereinthe automatically translating the first parameter settings into thesecond parameter settings includes multiplying the amplitude by a scalefactor, wherein the scale factor is determined based on whether thefirst parameter settings are characterized as cathodic, characterized asanodic, or characterized as balanced or relatively balanced betweencathodic and anodic.
 4. The method of claim 2, wherein the automaticallytranslating the first parameter settings into the second parametersettings includes multiplying the amplitude by a scale factor, whereinan equation is used to determine the scale factor using one or more of:the polarity within the first parameter settings; the amplitude withinthe first parameter settings; a pulse width within the first parametersettings; a waveform within the first parameter settings; a frequencywithin the first parameter settings; at least one burst-relatedparameter within the first parameter settings; or one or more locationsof modulation within the first parameter settings.
 5. The method ofclaim 2, wherein the automatically translating the first parametersettings into the second parameter settings includes multiplying theamplitude by a scale factor, wherein the scale factor is determinedusing a ratio between a first therapeutic range for the first parametersettings and a second therapeutic range for the second parametersettings, the first therapeutic range represents a range of amplitudesfor the first parameter settings that extends from a therapeuticthreshold to a side effect threshold, and the second therapeutic rangerepresents a range of amplitudes for the second parameter settings thatextends from a therapeutic threshold to a side effect threshold.
 6. Themethod of claim 2, wherein the automatically translating the firstparameter settings into the second parameter settings includesmultiplying the amplitude by a scale factor, wherein a look-up table isused to determine the scale factor, and at least one of the polarity,the pulse width, the amplitude, the waveform, the frequency, the atleast one burst-related parameter, or the one or more locations ofmodulation is used to index into the look-up table, the scale factorbeing a nearest value or an interpolated value.
 7. The method of claim2, further comprising: constructing a first table of I_(th) values forthe fractionalization in the first parameter settings to characterizespatial points, wherein the first parameter settings provide astimulation field model; determining a maximum radius of the stimulationfield model at the amplitude for the first parameter settings;constructing a second table of I_(th) values to characterize the spatialpoints for the second parameter settings to provide the stimulationfield model with the maximum radius; determining, within the secondtable, a minimum value that provides the stimulation field model thatequals or approximately equals the maximum radius, and using thedetermined minimum value, that provides the stimulation field model atthe radius, as an amplitude for the second parameter settings.
 8. Themethod of claim 2, further comprising: constructing a first tablecharacterizing spatial points for the fractionalization values in thefirst parameter settings, wherein the first parameter settings provide afirst stimulation field model; determining a volume of a stimulationfield model at the amplitude in the first parameter settings;constructing a second table characterizing the spatial points for thesecond parameter settings to provide a second stimulation field model;determining an amplitude that provides the second stimulation fieldmodel with a volume that equals or approximately equals the firststimulation field model; and using the determined amplitude in thesecond parameter settings.
 9. The method of claim 1, further comprising:receiving at least one target region to be targeted using aneuromodulation field and zero or more avoidance regions to be avoidedusing the neuromodulation field, and determining fractionalizationvalues for the second parameter settings to modulate the at least onetarget region and avoid the zero or more avoidance regions; andreceiving a polarity input indicating whether to provide anodicneuromodulation, cathodic neuromodulation or balanced or approximatelybalanced neuromodulation, and controlling a polarity of neuromodulationprovided by the neuromodulator according to the received polarity input.10. The method of claim 9, wherein the automatically translating thefirst parameter settings into the second parameter settings includeschanging the polarity of the neuromodulation, changing thefractionalization and changing the amplitude.
 11. The method of claim10, wherein the automatically translating the first parameter settingsinto the second parameter settings includes optimizing thefractionalization and the amplitude for the second parameter settings tomodulate a stimulation field model that corresponds to the firstparameter settings.
 12. The method of claim 10, wherein theautomatically translating the first parameter settings into the secondparameter settings includes: constructing a first I_(th) table for firstfractionalization values in the first parameter settings to characterizespatial points, wherein the first parameter settings have an amplitude,a pulse width and a frequency; determining initial secondfractionalization values for the second parameter settings based on thefirst fractionalization values, wherein elements of the initial secondfractionalization values have an opposite polarity with respect toelements of the first fractionalization values, and constructing asecond table using the initial second fractionalization values;determining a scaling factor for the initial fractionalization values inthe second table to provide a scaled second table, wherein the scalingfactor produces a minimum sum of the squares difference between thefirst table and the scaled second table; and optimizing the initialsecond fractionalization values into a second fractionalization valuesin the second table that has a least sum of the squared differencebetween the first table and the scaled second table.
 13. The method ofclaim 1, further comprising using a virtual electrode to index into thefractionalization look-up table to produce multipolarfractionalizations.
 14. The method of claim 1, further comprising usingsteering coordinates, including z axis, rotation, and spreadcoordinates, to index into the fractionalization look-up table toproduce monopolar fractionalizations.
 15. The method of claim 1, furthercomprising using a one-polarity major multipolar fractionalizationlook-up table to produce anodic major multipolar fractionalization orcathodic major multipolar fractionalization.
 16. The method of claim 1,further comprising using a monopolar look-up table to produce anodicmonopolar or cathodic monopolar fractionalizations.
 17. The method ofclaim 1, further comprising using a monopolar look-up table to producepolarity-balanced fractionalizations.
 18. The method of claim 1, furthercomprising receiving at least one of a polarity-specific regionincluding a target region to be targeted using a neuromodulation fieldof a polarity type and zero or more avoidance regions to be avoidedusing the neuromodulation field of the polarity type, wherein thepolarity-specific region is specific to one or more of cathodic majorneuromodulation, anodic major neuromodulation, or balanced or relativelybalanced neuromodulation, wherein the automatically translating thefirst parameter settings into the second parameter settings includesdetermining fractionalization values for the second parameter settingsbased on the polarity-specific region.
 19. A non-transitorycomputer-readable medium having computer executable instructions storedthereon that, when executed by at least one processor, cause the atleast one processor to perform the instructions to adjustneuromodulation parameters for use by a neuromodulator operablyconnected to a plurality of electrodes to modulate a neural target,including: determining that a translation trigger has occurred; and inresponse to determining that the translation trigger has occurred,automatically translating first parameter settings into second parametersettings, wherein the automatically translating the first parametersettings into the second parameter settings includes eitherautomatically translating from cathodic parameter settings to anodicparameter settings, or automatically translating from anodic parametersettings to cathodic parameter settings, wherein the neuromodulator isconfigured to use cathodic parameter settings for the neuromodulationparameters to deliver cathodic modulation to the neural target, and isconfigured to use anodic parameter settings for the neuromodulationparameters to deliver anodic modulation to the neural target.
 20. Thenon-transitory computer-readable medium of claim 19, wherein the firstparameter settings for the neuromodulation parameters include at least apolarity, an amplitude, and a fractionalization, the automaticallytranslating the first parameter settings into the second parametersettings includes changing the polarity for each active electrode.